The Perfect Package
Devin English
May 22, 2019
The Most Important Packages to Use for Beginners
These packages can get you started when working on data visualization as a beginner.
HOW TO INSTALL A PACKAGE
Use function **install.packages(“Name_of_package”“)** in console to install the package (you only need to do this once)
Call the package using library(Name_of_package) at the beginning of the script to use the package
DATA MANIPULATION
Dplyr
Dplyr is awesome to manipulate data structure in an easy and basic way!
Some functions include:
mutate() adds new variables that are functions of existing variables
select() picks column based on their name
filter() picks row based on their value
summarise() reduces rows to a single summary
arrange() changes row ordering
See documentation here
dplyr
Tidyr
Tidy allows you to organize your data structures in a organized way!
Some functions include:
gather() takes columns and gathers them into rows
spread() takes two columns and spreads into multiple columns
See documentation here 
GRAPHICS
Ggplot2
ggplot2 is the best package gor creating beautiful visualizations super easily!
How to Use:
- ggplot() is the outside function to use for your plot
- aes() is used for choosing axis variables and other ploting info
- geom_point() and geom_histogram() is used to select the type of graph to create
- scale_colour_brewer() can create beautiful color schemes
See documentation here
ggplot
ggplot
SYNTAX
Magrittr
Magrittr allows you to perform functions more efficiently
This package allows you to use piping in your code.
Piping uses the syntax %>% which takes one function or variable and pipes it in to be the first parameter of the second function.
An Example: Function 1 (2 * 4) %>% Function 2 (+ 3) = 11
See documentation here
DATASETS
A list of built in datasets to use for your next project!
- HairEyeColor Distribution of hair and eye color and sex in 592 statistics students.
- Sleep Data which show the effect of two soporific drugs (increase in hours of sleep compared to control) on 10 patients.
- Orange Records of the growth of orange trees.
- cars Speed and Stopping Distances of Cars.
- rivers Lengths (in miles) of 141 “major” rivers in North America.
- Presidents The (approximately) quarterly approval rating for the President of the United States.
Example scripts
add packages
library(ggplot2)
library(dplyr)
library(tidyr)
library(magrittr)dplyr
Adding an additional colum titled time my dividing every value of dist by every value of speed
knitr::kable(head(cars, 5))
knitr::kable(mutate(head(cars, 5), time = round((dist/speed), digit = 2)))| speed | dist |
|---|---|
| 4 | 2 |
| 4 | 10 |
| 7 | 4 |
| 7 | 22 |
| 8 | 16 |
| speed | dist | time |
|---|---|---|
| 4 | 2 | 0.50 |
| 4 | 10 | 2.50 |
| 7 | 4 | 0.57 |
| 7 | 22 | 3.14 |
| 8 | 16 | 2.00 |
tidyr
Rearranging table to be grouped by ID with the extra amount of sleep becoming the value for the columns of groups
knitr::kable(sleep)
knitr::kable(sleep %>%
spread(group, extra))| extra | group | ID |
|---|---|---|
| 0.7 | 1 | 1 |
| -1.6 | 1 | 2 |
| -0.2 | 1 | 3 |
| -1.2 | 1 | 4 |
| -0.1 | 1 | 5 |
| 3.4 | 1 | 6 |
| 3.7 | 1 | 7 |
| 0.8 | 1 | 8 |
| 0.0 | 1 | 9 |
| 2.0 | 1 | 10 |
| 1.9 | 2 | 1 |
| 0.8 | 2 | 2 |
| 1.1 | 2 | 3 |
| 0.1 | 2 | 4 |
| -0.1 | 2 | 5 |
| 4.4 | 2 | 6 |
| 5.5 | 2 | 7 |
| 1.6 | 2 | 8 |
| 4.6 | 2 | 9 |
| 3.4 | 2 | 10 |
| ID | 1 | 2 |
|---|---|---|
| 1 | 0.7 | 1.9 |
| 2 | -1.6 | 0.8 |
| 3 | -0.2 | 1.1 |
| 4 | -1.2 | 0.1 |
| 5 | -0.1 | -0.1 |
| 6 | 3.4 | 4.4 |
| 7 | 3.7 | 5.5 |
| 8 | 0.8 | 1.6 |
| 9 | 0.0 | 4.6 |
| 10 | 2.0 | 3.4 |
ggplot2
Creates a scatter plot of iris data
plot <- ggplot(iris) +
geom_point(aes(x = Petal.Length, y = Petal.Width, color = Species)) +
scale_color_brewer(palette="Set2") +
labs(title = "Petal Length VS Petal Width of Different Iris Species")
print(plot)
